Summary of Can Large Language Models Generalize Analogy Solving Like People Can?, by Claire E. Stevenson et al.
Can Large Language Models generalize analogy solving like people can?
by Claire E. Stevenson, Alexandra Pafford, Han L. J. van der Maas, Melanie Mitchell
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers investigate whether large language models (LLMs) can generalize solving analogies to new domains like humans do. Analogies involve transferring information from a known context to a new one through abstract rules and relational similarity. While LLMs can solve various forms of analogies, they struggle with robust human-like analogical transfer. The study finds that children, adults, and LLMs are compared on solving letter-string analogies in familiar (Latin alphabet) and unfamiliar domains (Greek alphabet and symbols). Children and adults easily generalize their knowledge to new domains, whereas LLMs do not. This key difference highlights the limitations of current AI models in replicating human-like analogical transfer abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Analogies are a way for our brains to figure out new information by using rules we learned before. For example, if you know that “body has feet,” you can use that rule to understand that “table has legs.” Children usually learn how to solve analogies around age 5-6, and it helps them with other tasks too. Scientists have found that big computers (like Google) can also solve some types of analogies, but they don’t do as well as people do. In this study, researchers compare how children, adults, and these computer models do when solving a type of analogy problem called “letter-string analogies.” They use different letters to test if the computer models can learn from one set of letters and apply it to another set. The results show that humans are much better at learning and applying rules in new situations, while computers struggle to do so. |
